Wavelet Based Lossless DNA Sequence Compression for Faster Detection of Eukaryotic Protein Coding Regions

Abstract

Discrimination of protein coding regions called exons from noncoding regions called introns or junk DNA in eukaryotic cell is a computationally intensive task. But the dimension of the DNA string is huge; hence it requires large computation time. Further the DNA sequences are inherently random and have vast redundancy, hidden regularities, long repeats and complementary palindromes and therefore cannot be compressed efficiently. The objective of this study is to present an integrated signal processing algorithm that considerably reduces the computational load by compressing the DNA sequence effectively and aids the problem of searching for coding regions in DNA sequences. The presented algorithm is based on the Discrete Wavelet Transform (DWT), a very fast and effective method used for data compression and followed by comb filter for effective prediction of protein coding period-3 regions in DNA sequences. This algorithm is validated using standard dataset such as HMR195, Burset and Guigo and KEGG.

Haar wavelet based approach for Short Tandem Repeats(STR) Detection

“…Wavelet transforms was applied into the genome in some early studies [5,9,[18][19][20][21]. Wavelet is the rapidly decaying wave-like oscillation that has zero mean infinite duration.…”

“…It filters the long interval signal and short interval signal for getting low and high-frequency information respectively [22]. A signal 'x' pass through the low pass filter (scaling function) and high pass filter(wavelet function) and impulse response 'g' convolute with each of the filtered signal [20]. A wavelet transforms data or signals transform into coefficients based signal on wavelet transforms; it is a lossless linear transform.…”

Signal Processing Approach for Recognizing Identical Reads From DNA Sequencing of Bacillus Strains

“…A wavelet transform is a transformation of a signal or data into time-scale domain on a basis of wavelet functions [7][8]. The wavelet transform representations enable exploring the hidden information about the signal.…”